Modeling to Engineering and Failure Times Data: Using the Marshall–Olkin Extended Exponential Distribution

In this article, we study and introduce the Marshall–Olkin extended exponential (MO‐ExE) distribution, a novel three‐parameter life expectancy distribution. The new model is characterized by high adaptability in data analysis. Various mathematical properties, including ordinary moments, moment‐generating functions (MGFs), and conditional moments (CMs) of the new suggested model, were computed. The model parameters are estimated using the maximum likelihood method. A simulation study is evaluated to demonstrate the behavior of the parameter model. In the final analysis, we demonstrate the significance of the novel model by examining practical data applications, thus, showing the adaptability and potential of the MO‐ExE model.

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Publication Details

Journal
Advances in Mathematical Physics
Published
2025-01-01
DOI
https://doi.org/10.1155/admp/6659119
Citations
1
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
2.63

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article

Modeling to Engineering and Failure Times Data: Using the Marshall–Olkin Extended Exponential Distribution

Suleman Nasiru, Ibrahim E. Ragab, Mohammed Elgarhy, Mustapha Muhammad et al.
1 citations
Advances in Mathematical Physics
Statistical Distribution Estimation and Applications
2.63
article

Modeling to Engineering and Failure Times Data: Using the Marshall–Olkin Extended Exponential Distribution

Suleman Nasiru, Ibrahim E. Ragab, Mohammed Elgarhy, Mustapha Muhammad, Najwan Alsadat
article en
1 citations

Abstract

In this article, we study and introduce the Marshall–Olkin extended exponential (MO‐ExE) distribution, a novel three‐parameter life expectancy distribution. The new model is characterized by high adaptability in data analysis. Various mathematical properties, including ordinary moments, moment‐generating functions (MGFs), and conditional moments (CMs) of the new suggested model, were computed. The model parameters are estimated using the maximum likelihood method. A simulation study is evaluated to demonstrate the behavior of the parameter model. In the final analysis, we demonstrate the significance of the novel model by examining practical data applications, thus, showing the adaptability and potential of the MO‐ExE model.

Advances in Mathematical PhysicsVol. 2025(1)
King Saud University (SA), Guangdong University of Petrochemical Technology (CN), Egyptian Institute of Alexandria Academy for Administration & Accounting (EG), High Institute for Management Sciences (EG), C.K. Tedam University of Technology and Applied Sciences (GH)
King Saud University
Openalex Percentile: Top 10%
Statistical Distribution Estimation and Applications
2.63
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Modeling to Engineering and Failure Times Data: Using the Marshall–Olkin Extended Exponential Distribution — Suleman Nasiru, Ibrahim E. Ragab, et al. · Advances in Mathematical Physics (2025) | TGRS Research Map | TGRS